TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification

Abstract

Abstract Background Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice. Results We develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes. The performance of TruNeo and other algorithms were compared based on data from published literature as well as raw data from a lung cancer patient. Recall rate of immunogenic ones among the top 10-ranked neoantigens were compared based on the published combined data set. Recall rate of TruNeo was 52.63%, which was 2.5 times higher than that predicted by MHCflurry (21.05%), and 2 times higher than NetMHCpan 4 (26.32%). Furthermore, the positive rate of top 10-ranked neoantigens for the lung cancer patient were compared, showing a 50% positive rate identified by TruNeo, which was 2.5 times higher than that predicted by MHCflurry (20%). Conclusions TruNeo, which considers multiple biological processes rather than peptide-MHC binding affinity prediction only, provides prioritization of candidate neoantigens with high immunogenicity for neoantigen-targeting personalized immunotherapies.